U.S. patent number 10,908,247 [Application Number 16/418,909] was granted by the patent office on 2021-02-02 for system and method for texture analysis in magnetic resonance fingerprinting (mrf).
This patent grant is currently assigned to CASE WESTERN RESERVE UNIVERSITY. The grantee listed for this patent is Case Western Reserve University. Invention is credited to Chaitra Badve, Samuel Frankel, Vikas Gulani, Debra McGivney, Ananya Panda.
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United States Patent |
10,908,247 |
McGivney , et al. |
February 2, 2021 |
System and method for texture analysis in magnetic resonance
fingerprinting (MRF)
Abstract
A method for characterizing a tissue in a subject using magnetic
resonance fingerprinting (MRF) includes acquiring MRF data from a
tissue in a subject using a magnetic resonance imaging (MRI)
system, comparing the MRF data to a MRF dictionary to identify
quantitative values of at least one tissue property for the MRF
data, generating a quantitative map based on the quantitative
values of the at least one tissue property, identifying at least
one region of interest on the quantitative map, determining at
least one texture feature of the at least one region of interest of
the quantitative map, characterizing the tissue in the at least one
region of interest based on the at least one texture feature and
generating a report indicating the characterization of the tissue
based in the at least one texture feature.
Inventors: |
McGivney; Debra (Bay Village,
OH), Frankel; Samuel (Cleveland Heights, OH), Panda;
Ananya (Cleveland, OH), Badve; Chaitra (Beachwood,
OH), Gulani; Vikas (Shaker Heights, OH) |
Applicant: |
Name |
City |
State |
Country |
Type |
Case Western Reserve University |
Cleveland |
OH |
US |
|
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Assignee: |
CASE WESTERN RESERVE UNIVERSITY
(Cleveland, OH)
|
Family
ID: |
1000005336012 |
Appl.
No.: |
16/418,909 |
Filed: |
May 21, 2019 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20190353738 A1 |
Nov 21, 2019 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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62674548 |
May 21, 2018 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01R
33/50 (20130101); G01R 33/5608 (20130101); G01R
33/4828 (20130101); G01R 33/543 (20130101) |
Current International
Class: |
G01R
33/56 (20060101); G01R 33/48 (20060101); G01R
33/54 (20060101); G01R 33/50 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Bates, A. et al. Prostate-specific membrane antigen PET/MRI
validation of MR textural analysis for detection of transition zone
prostate cancer. European Radiology, 2017;Epub. cited by applicant
.
Haralick, R. Textural features for image classification. IEEE
Transactions on Systems, Man and Cybernetics, 1973:6;610-621. cited
by applicant .
Khalvati, F. et al., Automated prostate cancer detection via
comprehensive multi-parametric magnetic resonance Imaging texture
feature models. BMC Medical Imaging 2015;15:27. cited by applicant
.
Ma, D., et al. Magnetic resonance fingerprinting. Nature,
2013;495(7440):187-192. cited by applicant .
Nketiah, G., et al. T2-weighted MRI-derived textural features
reflect prostate cancer aggressiveness: preliminary results.
European Radiology, 2017;27(7):3050-3059. cited by applicant .
Wibmer, A. et al., "Haralick texture analysis of prostate MRI:
utility for differentiating non-cancerous prostate from prostate
cancer and differentiating prostate cancers with different Gleason
scores." European radiology 25.10 (2015): 2840-2850. cited by
applicant .
Yu, A. C., et al. Development of a combined MR fingerprinting and
diffusion examination for prostate cancer. Radiology
2017;283(3):729-738. cited by applicant.
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Primary Examiner: Patidar; Jay
Attorney, Agent or Firm: Quarles & Brady LLP
Government Interests
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH
This invention was made with government support under EB016728 and
CA208236 awarded by the National Institutes of Health. The
government has certain rights in the invention.
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATION
This application is based on, claims priority to, and incorporates
herein by reference in its entirety U.S. Ser. No. 62/674,548 filed
May 21, 2018, and entitled "System And Method For Texture Analysis
In Magnetic Resonance Fingerprinting (MRF)."
Claims
The invention claimed is:
1. A method for characterizing a tissue in a subject using magnetic
resonance fingerprinting (MRF), the method comprising: acquiring
MRF data from the tissue in the subject using a magnetic resonance
imaging (MRI) system; comparing the MRF data to a MRF dictionary to
identify quantitative values of at least one tissue property for
the MRF data; generating a quantitative map based on the
quantitative values of the at least one tissue property;
identifying at least one region of interest on the quantitative
map; determining at least one texture feature of the at least one
region of interest of the quantitative map; characterizing the
tissue in the at least one region of interest based on the at least
one texture feature; and generating a report indicating the
characterization of the tissue based in the at least one texture
feature.
2. The method according to claim 1, wherein the at least one tissue
property is T1.
3. The method according to claim 1, wherein the at least one tissue
property is T2.
4. The method according to claim 1, wherein determining at least
one texture feature includes calculating the at least one texture
feature using Gray Level Co-occurrence Matrices.
5. The method according to claim 4, wherein the at least one
texture feature is one of autocorrelation, cluster prominence,
cluster shade, contrast, correlation, difference entropy,
difference variance, dissimilarity, energy, entropy, homogeneity,
information measure of correlation 1, information measure of
correlation 2, inverse difference, maximum probability, sum
average, sum entropy, sum of squares: variance, and sum
variance.
6. The method according to claim 1, wherein determining at least
one texture feature includes calculating the at least one texture
feature using Gray Level Run Length Matrices.
7. The method according to claim 6, wherein the at least one
texture features is one of short run emphasis, long run emphasis,
gray level non-uniformity, run percentage, run length
non-uniformity, low gray level run emphasis, and high gray level
run emphasis.
8. The method according to claim 1, wherein the characterization
identifies the presence of a cancerous tissue in the region of
interest.
9. The method according to claim 8, wherein the characterization
identifies a grade of the cancerous tissue in the region of
interest.
10. The method according to claim 1, wherein the tissue is prostate
tissue.
11. The method according to claim 1, wherein characterizing the
tissue in the at least one region of interest based on the at least
one texture feature includes performing Student's t-tests on the at
least one texture feature.
12. A magnetic resonance fingerprinting (MRF) system comprising: a
magnet system configured to generate a polarizing magnetic field
about at least a portion of a subject; a magnetic gradient system
including a plurality of magnetic gradient coils configured to
apply at least one magnetic gradient field to the polarizing
magnetic field; a radio frequency (RF) system configured to apply
an RF field to the subject and to receive magnetic resonance
signals from the subject using a coil array; and a computer system
programmed to: acquire MRF data from a tissue in a subject; compare
the MRF data to a MRF dictionary to identify quantitative values of
at least one tissue property for the MRF data; generate a
quantitative map based on the quantitative values of the at least
one tissue property; identify at least one region of interest on
the quantitative map; determine at least one texture feature of the
at least one region of interest of the quantitative map;
characterize the tissue in the at least one region of interest
based on the at least one texture feature; and generate a report
indicating the characterization of the tissue based in the at least
one texture feature.
13. The system according to claim 12, wherein the at least one
tissue property is T1.
14. The system according to claim 12, wherein the at least one
tissue property is T2.
15. The system according to claim 12, wherein determining at least
one texture feature includes calculating the at least one texture
feature using Gray Level Co-occurrence Matrices.
16. The system according to claim 12, wherein determining at least
one texture feature includes calculating the at least one texture
feature using Gray Level Run Length Matrices.
17. The system according to claim 12, wherein the characterization
identifies the presence of a cancerous tissue in the region of
interest.
18. The system according to claim 17, wherein the characterization
identifies a grade of the cancerous tissue in the region of
interest.
19. The system according to claim 12, wherein the tissue is
prostate tissue.
20. The method according to claim 12, wherein characterizing the
tissue in the at least one region of interest based on the at least
one texture feature includes performing Student's t-tests on the at
least one texture feature.
Description
BACKGROUND
Characterizing tissue species using nuclear magnetic resonance
("NMR") can include identifying different properties of a resonant
species (e.g., T1 spin-lattice relaxation, T2 spin-spin relaxation,
proton density). Other properties like tissue types and
super-position of attributes can also be identified using NMR
signals. These properties and others may be identified
simultaneously using magnetic resonance fingerprinting ("MRF"),
which is described, as one example, by D. Ma, et al., in "Magnetic
Resonance Fingerprinting," Nature, 2013; 495 (7440): 187-192.
Conventional magnetic resonance imaging ("MM") pulse sequences
include repetitive similar preparation phases, waiting phases, and
acquisition phases that serially produce signals from which images
can be made. The preparation phase determines when a signal can be
acquired and determines the properties of the acquired signal. For
example, a first pulse sequence may produce a T1-weighted signal at
a given echo time ("TE"), while a second pulse sequence may produce
a T2-weighted signal at a different (or second) TE. These
conventional pulse sequences typically provide qualitative results
where data are acquired with various weighting or contrasts that
highlight a particular parameter (e.g., T1 relaxation, T2
relaxation).
When magnetic resonance ("MR") images are generated, they may be
viewed by a radiologist and/or surgeon who interprets the
qualitative images for specific disease signatures. The radiologist
may examine multiple image types (e.g., T1-weighted, T2 weighted)
acquired in multiple imaging planes to make a diagnosis. The
radiologist or other individual examining the qualitative images
may need particular skill to be able to assess changes from session
to session, from machine to machine, and from machine configuration
to machine configuration.
Unlike conventional MM, MRF employs a series of varied sequence
blocks that simultaneously produce different signal evolutions in
different resonant species (e.g., tissues) to which the radio
frequency ("RF") is applied. The signals from different resonant
tissues will, however, be different and can be distinguished using
MRF. The different signals can be collected over a period of time
to identify a signal evolution for the volume. Resonant species in
the volume can then be characterized by comparing the signal
evolution to known signal evolutions. Characterizing the resonant
species may include identifying a material or tissue type, or may
include identifying MR parameters associated with the resonant
species. The "known" evolutions may be, for example, simulated
evolutions calculated from physical principles and/or previously
acquired evolutions. A large set of known evolutions may be stored
in a dictionary.
Magnetic resonance imaging (MM) techniques and MRF techniques have
been employed to attempt to differentiate normal tissue from
prostate cancer. For example, MRI techniques such as conventional
T2 weighted images, diffusion weighted images (DWI) with apparent
diffusion coefficient (ADC) mapping, dynamic contrast-enhanced MRI
(DCE-MRI), and MR spectroscopy (MRS) have been used for
differentiating normal tissue form prostate cancer. Conventional
MRI techniques, however, are limited by the qualitative nature of
the images and the resulting subjective analysis of the images. For
example, different reviewers (e.g., a radiologist or surgeon) of
the images may arrive at different diagnoses when reading the same
image. To improve analysis of qualitative MM images (e.g.,
T1-weighted and T2-weighted) and ADC maps used for differentiating
normal tissue and prostate cancer, patterns of "pixel attributes",
or texture analysis, may be used to identify prostate cancer. For
example, second order statistical features, or "texture features"
characterize pairs of pixels in an image, providing statistical
information undetectable to the naked eye.
Previous MRF techniques have utilized a combination of MRF-based
quantitative T1 and T2 values and MRI-based quantitative ADC values
to differentiate between normal tissue and prostate cancer (e.g.,
between normal peripheral zone (PZ) tissue and PZ prostate cancer)
and to differentiate between different Gleason scores or grades for
prostate cancer. However, some residual overlap may be present in
such analyses, for example, between prostatitis and prostate
cancer. Prostatitis appears similar to prostate cancer in MRI and
may lead to unnecessary biopsies of benign prostate. Texture
analysis has not previously been applied to prostate tissue
relaxometry maps.
SUMMARY OF THE DISCLOSURE
In accordance with an embodiment, a method for characterizing a
tissue in a subject using magnetic resonance fingerprinting (MRF)
includes acquiring MRF data from a tissue in a subject using a
magnetic resonance imaging (MRI) system, comparing the MRF data to
a MRF dictionary to identify quantitative values of at least one
tissue property for the MRF data, generating a quantitative map
based on the quantitative values of the at least one tissue
property, identifying at least one region of interest on the
quantitative map, determining at least one texture feature of the
at least one region of interest of the quantitative map,
characterizing the tissue in the at least one region of interest
based on the at least one texture feature and generating a report
indicating the characterization of the tissue based in the at least
one texture feature.
In accordance with another embodiment, a magnetic resonance
fingerprinting (MRF) system includes a magnet system configured to
generate a polarizing magnetic field about at least a portion of a
subject, a magnetic gradient system including a plurality of
magnetic gradient coils configured to apply at least one magnetic
gradient field to the polarizing magnetic field and a radio
frequency (RF) system configured to apply an RF field to the
subject and to receive magnetic resonance signals from the subject
using a coil array. The system further includes a computer system
programmed to acquire MRF data from a tissue in a subject, compare
the MRF data to a MRF dictionary to identify quantitative values of
at least one tissue property for the MRF data, generate a
quantitative map based on the quantitative values of the at least
one tissue property, identify at least one region of interest on
the quantitative map, determine at least one texture feature of the
at least one region of interest of the quantitative map,
characterize the tissue in the at least one region of interest
based on the at least one texture feature and generate a report
indicating the characterization of the tissue based in the at least
one texture feature.
BRIEF DESCRIPTION OF THE DRAWINGS
The present invention will hereafter be described with reference to
the accompanying drawings, wherein like reference numerals denote
like elements.
FIG. 1 is a schematic diagram of an example MM system in accordance
with an embodiment;
FIG. 2 illustrates a method for magnetic resonance fingerprinting
with texture analysis in accordance with an embodiment;
FIG. 3 shows an example MRF-based T2 map of with a region of
interest (ROI) drawn on a transition zone lesion and the ROI of the
lesion after conversion to 4-bit gray level image in accordance
with an embodiment;
FIG. 4 illustrates an example Gray Level Co-occurrence Matrix
(GLGM) in accordance with an embodiment;
FIG. 5 illustrates an example Gray Level Run Length Matrix (GLRLM)
in accordance with an embodiment;
FIGS. 6A-6D illustrate example boxplots of texture features for
comparison of significant texture features between cancer and
non-cancer transition zone (TZ) regions of interest;
FIG. 7 illustrates an example Receiver Operating Characteristic
(ROC) curve for two texture features; and
FIG. 8 illustrates an example ROC curve for two texture
features.
DETAILED DESCRIPTION
Magnetic resonance fingerprinting ("MRF") is a technique that
facilitates mapping of tissue or other material properties based on
random or pseudorandom measurements of the subject or object being
imaged. In particular, MRF can be conceptualized as employing a
series of varied "sequence blocks" that simultaneously produce
different signal evolutions in different "resonant species" to
which the RF is applied. The term "resonant species," as used
herein, refers to a material, such as water, fat, bone, muscle,
soft tissue, and the like, that can be made to resonate using NMR.
By way of illustration, when radio frequency ("RF") energy is
applied to a volume that has both bone and muscle tissue, then both
the bone and muscle tissue will produce a nuclear magnetic
resonance ("NMR") signal; however, the "bone signal" represents a
first resonant species and the "muscle signal" represents a second
resonant species, and thus the two signals will be different. These
different signals from different species can be collected
simultaneously over a period of time to collect an overall "signal
evolution" for the volume.
The measurements obtained in MRF techniques are achieved by varying
the acquisition parameters from one repetition time ("TR") period
to the next, which creates a time series of signals with varying
contrast. Examples of acquisition parameters that can be varied
include flip angle ("FA"), RF pulse phase, TR, echo time ("TE`),
and sampling patterns, such as by modifying one or more readout
encoding gradients. The acquisition parameters are varied in a
random manner, pseudorandom manner, or other manner that results in
signals from different materials or tissues to be spatially
incoherent, temporally incoherent, or both. For example, in some
instances, the acquisition parameters can be varied according to a
non-random or non-pseudorandom pattern that otherwise results in
signals from different materials or tissues to be spatially
incoherent, temporally incoherent, or both.
From these measurements, which as mentioned above may be random or
pseudorandom, or may contain signals from different materials or
tissues that are spatially incoherent, temporally incoherent, or
both, MRF processes can be designed to map any of a wide variety of
parameters. Examples of such parameters that can be mapped may
include, but are not limited to, longitudinal relaxation time
(T.sub.1), transverse relaxation time (T.sub.2), main or static
magnetic field map (B.sub.0), and proton density (.rho.). MRF is
generally described in U.S. Pat. No. 8,723,518 and Published U.S.
Patent Application No. 2015/0301141, each of which is incorporated
herein by reference in its entirety.
The data acquired with MRF techniques are compared with a
dictionary of signal models, or templates, that have been generated
for different acquisition parameters from magnetic resonance signal
models, such as Bloch equation-based physics simulations. This
comparison allows estimation of the physical parameters, such as
those mentioned above. As an example, the comparison of the
acquired signals to a dictionary can be performed using any
suitable matching or pattern recognition technique. The parameters
for the tissue or other material in a given voxel are estimated to
be the values that provide the best signal template matching. For
instance, the comparison of the acquired data with the dictionary
can result in the selection of a signal vector, which may
constitute a weighted combination of signal vectors, from the
dictionary that best corresponds to the observed signal evolution.
The selected signal vector includes values for multiple different
quantitative parameters, which can be extracted from the selected
signal vector and used to generate the relevant quantitative
parameter maps.
The stored signals and information derived from reference signal
evolutions may be associated with a potentially very large data
space. The data space for signal evolutions can be partially
described by:
.times..times..times..times..times..times..function..alpha..times..functi-
on..alpha..PHI..times..function..times..function..times.
##EQU00001## where SE is a signal evolution; N.sub.S is a number of
spins; N.sub.A is a number of sequence blocks; N.sub.RF is a number
of RF pulses in a sequence block; .alpha. is a flip angle; .PHI. is
a phase angle; R.sub.i(.alpha.) is a rotation due to off resonance;
R.sub.RF.sub.ij(.alpha.,.PHI.) is a rotation due to RF differences;
R(G) is a rotation due to a magnetic field gradient; T.sub.1 is a
longitudinal, or spin-lattice, relaxation time; T.sub.2 is a
transverse, or spin-spin, relaxation time; D is diffusion
relaxation; E.sub.i(T.sub.1, T.sub.2, D) is a signal decay due to
relaxation differences; and M.sub.0 is the magnetization in the
default or natural alignment to which spins align when placed in
the main magnetic field.
While E.sub.i(T.sub.1, T.sub.2, D) is provided as an example, in
different situations, the decay term, E.sub.i(T.sub.1, T.sub.2, D),
may also include additional terms, E.sub.i(T.sub.1, T.sub.2, D, . .
. ) or may include fewer terms, such as by not including the
diffusion relaxation, as E.sub.i(T.sub.1, T.sub.2) or
E.sub.i(T.sub.1, T.sub.2, . . . ). Also, the summation on "j" could
be replace by a product on "j". The dictionary may store signals
described by, S.sub.i=R.sub.iE.sub.i(S.sub.i-1) (2); where S.sub.0
is the default, or equilibrium, magnetization; S.sub.i is a vector
that represents the different components of magnetization, M.sub.x,
M.sub.y, and M.sub.z during the i.sup.th acquisition block; R.sub.i
is a combination of rotational effects that occur during the
i.sup.th acquisition block; and E.sub.i is a combination of effects
that alter the amount of magnetization in the different states for
the i.sup.th acquisition block. In this situation, the signal at
the i.sup.th acquisition block is a function of the previous signal
at acquisition block (i.e., the (i-1).sup.th acquisition block).
Additionally or alternatively, the dictionary may store signals as
a function of the current relaxation and rotation effects and of
previous acquisitions. Additionally or alternatively, the
dictionary may store signals such that voxels have multiple
resonant species or spins, and the effects may be different for
every spin within a voxel. Further still, the dictionary may store
signals such that voxels may have multiple resonant species or
spins, and the effects may be different for spins within a voxel,
and thus the signal may be a function of the effects and the
previous acquisition blocks.
Thus, in MRF, a unique signal timecourse is generated for each
pixel. This timecourse evolves based on both physiological tissue
properties such as T1 or T2 as well as acquisition parameters like
flip angle (FA) and repetition time (TR). This signal timecourse
can, thus, be referred to as a signal evolution and each pixel can
be matched to an entry in the dictionary, which is a collection of
possible signal evolutions or timecourses calculated using a range
of possible tissue property values and knowledge of the quantum
physics that govern the signal evolution. Upon matching the
measured signal evolution/timecourse to a specific dictionary
entry, the tissue properties corresponding to that dictionary entry
can be identified. A fundamental criterion in MRF is that spatial
incoherence be maintained to help separate signals that are mixed
due to undersampling. In other words, signals from various
locations should differ from each other, in order to be able to
separate them when aliased.
To achieve this process, a magnetic resonance imaging (MRI) system
or nuclear magnetic resonance (NMR) system may be utilized. FIG. 1
shows an example of an MRI system 100 in accordance with an
embodiment. MRI system 100 may be used to implement the methods
described herein. MRI system 100 includes an operator workstation
102, which may include a display 104, one or more input devices 106
(e.g., a keyboard, a mouse), and a processor 108. The processor 108
may include a commercially available programmable machine running a
commercially available operating system. The operator workstation
102 provides an operator interface that facilitates entering scan
parameters into the MM system 100. The operator workstation 102 may
be coupled to different servers, including, for example, a pulse
sequence server 110, a data acquisition server 112, a data
processing server 114, and a data store server 116. The operator
workstation 102 and the servers 110, 112, 114, and 116 may be
connected via a communication system 140, which may include wired
or wireless network connections.
The pulse sequence server 110 functions in response to instructions
provided by the operator workstation 102 to operate a gradient
system 118 and a radiofrequency ("RF") system 120. Gradient
waveforms for performing a prescribed scan are produced and applied
to the gradient system 118, which then excites gradient coils in an
assembly 122 to produce the magnetic field gradients G.sub.x,
G.sub.y, and G.sub.z that are used for spatially encoding magnetic
resonance signals. The gradient coil assembly 122 forms part of a
magnet assembly 124 that includes a polarizing magnet 126 and a
whole-body RF coil 128.
RF waveforms are applied by the RF system 120 to the RF coil 128,
or a separate local coil to perform the prescribed magnetic
resonance pulse sequence. Responsive magnetic resonance signals
detected by the RF coil 128, or a separate local coil, are received
by the RF system 120. The responsive magnetic resonance signals may
be amplified, demodulated, filtered, and digitized under direction
of commands produced by the pulse sequence server 110. The RF
system 120 includes an RF transmitter for producing a wide variety
of RF pulses used in MM pulse sequences. The RF transmitter is
responsive to the prescribed scan and direction from the pulse
sequence server 110 to produce RF pulses of the desired frequency,
phase, and pulse amplitude waveform. The generated RF pulses may be
applied to the whole-body RF coil 128 or to one or more local coils
or coil arrays.
The RF system 120 also includes one or more RF receiver channels.
An RF receiver channel includes an RF preamplifier that amplifies
the magnetic resonance signal received by the coil 128 to which it
is connected, and a detector that detects and digitizes the I and Q
quadrature components of the received magnetic resonance signal.
The magnitude of the received magnetic resonance signal may,
therefore, be determined at a sampled point by the square root of
the sum of the squares of the I and Q components: M= {square root
over (I.sup.2+Q.sup.2)} (3); and the phase of the received magnetic
resonance signal may also be determined according to the following
relationship:
.phi..function. ##EQU00002##
The pulse sequence server 110 may receive patient data from a
physiological acquisition controller 130. By way of example, the
physiological acquisition controller 130 may receive signals from a
number of different sensors connected to the patient, including
electrocardiograph ("ECG") signals from electrodes, or respiratory
signals from a respiratory bellows or other respiratory monitoring
devices. These signals may be used by the pulse sequence server 110
to synchronize, or "gate," the performance of the scan with the
subject's heart beat or respiration.
The pulse sequence server 110 may also connect to a scan room
interface circuit 132 that receives signals from various sensors
associated with the condition of the patient and the magnet system.
Through the scan room interface circuit 132, a patient positioning
system 134 can receive commands to move the patient to desired
positions during the scan.
The digitized magnetic resonance signal samples produced by the RF
system 120 are received by the data acquisition server 112. The
data acquisition server 112 operates in response to instructions
downloaded from the operator workstation 102 to receive the
real-time magnetic resonance data and provide buffer storage, so
that data is not lost by data overrun. In some scans, the data
acquisition server 112 passes the acquired magnetic resonance data
to the data processor server 114. In scans that require information
derived from acquired magnetic resonance data to control the
further performance of the scan, the data acquisition server 112
may be programmed to produce such information and convey it to the
pulse sequence server 110. For example, during pre-scans, magnetic
resonance data may be acquired and used to calibrate the pulse
sequence performed by the pulse sequence server 110. As another
example, navigator signals may be acquired and used to adjust the
operating parameters of the RF system 120 or the gradient system
118, or to control the view order in which k-space is sampled. In
still another example, the data acquisition server 112 may also
process magnetic resonance signals used to detect the arrival of a
contrast agent in a magnetic resonance angiography ("MRA") scan.
For example, the data acquisition server 112 may acquire magnetic
resonance data and processes it in real-time to produce information
that is used to control the scan.
The data processing server 114 receives magnetic resonance data
from the data acquisition server 112 and processes the magnetic
resonance data in accordance with instructions provided by the
operator workstation 102. Such processing may include, for example,
reconstructing two-dimensional or three-dimensional images by
performing a Fourier transformation of raw k-space data, performing
other image reconstruction algorithms (e.g., iterative or
backprojection reconstruction algorithms), applying filters to raw
k-space data or to reconstructed images, generating functional
magnetic resonance images, or calculating motion or flow
images.
Images reconstructed by the data processing server 114 are conveyed
back to the operator workstation 102 for storage. Real-time images
may be stored in a data base memory cache, from which they may be
output to operator display 102 or a display 136. Batch mode images
or selected real time images may be stored in a host database on
disc storage 138. When such images have been reconstructed and
transferred to storage, the data processing server 114 may notify
the data store server 116 on the operator workstation 102. The
operator workstation 102 may be used by an operator to archive the
images, produce films, or send the images via a network to other
facilities.
The MRI system 100 may also include one or more networked
workstations 142. For example, a networked workstation 142 may
include a display 144, one or more input devices 146 (e.g., a
keyboard, a mouse), and a processor 148. The networked workstation
142 may be located within the same facility as the operator
workstation 102, or in a different facility, such as a different
healthcare institution or clinic.
The networked workstation 142 may gain remote access to the data
processing server 114 or data store server 116 via the
communication system 140. Accordingly, multiple networked
workstations 142 may have access to the data processing server 114
and the data store server 116. In this manner, magnetic resonance
data, reconstructed images, or other data may be exchanged between
the data processing server 114 or the data store server 116 and the
networked workstations 142, such that the data or images may be
remotely processed by a networked workstation 142.
The present disclosure describes a system and method for applying
texture analysis to MRF T1 and T2 maps to improve upon the
quantitative MRF T1 and T2 data and to, for example, further
characterize prostate pathologies. In an embodiment, MRF may be
utilized with texture analysis to differentiate between prostate
cancer, prostatitis, and normal prostate tissue in patients with
suspected prostate cancer as well as to differentiate between
different grades of prostate cancer. For example, texture analysis
of prostate MRF T1 and T2 data allows quantitative differentiation
between low grade cancer and immediate/high grade cancer in the
peripheral zone. In another example, texture analysis of prostate
MRF T1 and T2 data allows quantitative differentiation between low
grade cancer and prostatitis in the transition zone, between
intermediate grade cancer and prostatitis in the transition zone,
between a combined set of all grades of cancer and prostatitis in
the transition zone, and between a combined set of all grades of
cancer and normal transition zone tissue. The robust nature of MRF
allows texture analysis to be consistently compared between image
series.
FIG. 2 illustrates a method for magnetic resonance fingerprinting
with texture analysis in accordance with an embodiment. While the
embodiments and examples herein are discussed with regard to the
prostate and prostate cancer, it should be understood that the
system and method disclosed may also be used to characterize
tissues of other anatomy and other types of disease. At block 202,
MR data is acquired from a region of interest in a subject using,
for example, an MRI system (e.g., MRI system 100 shown in FIG. 1).
In an embodiment, the MR data is acquired using a diffusion
weighted imaging (DWI) acquisition so the MR data may be used for
quantitative apparent diffusion coefficient (ADC) mapping. For
example, the MR data may be acquired using a high resolution T2 w,
diffusion weighted imaging using echo planar imaging. At block 204,
the MR data are used to determine quantitative ADC values. The ADC
is the measure of the mobility of diffusion of water molecules in
tissue. The MR data acquired with diffusion weighted imaging may be
used to calculate the ADC. The ADC value may be assessed using, for
example, different b values via changing gradient amplitudes. In
one example, the acquisition (block 202) uses b values of 50-1400
s/mm.sup.2. The ADC values determined at block 204 may be displayed
as a parametric map that reflects the degree of diffusion of water
molecules trough different tissues.
At block 206, a MRF dictionary is accessed. As used herein, the
term "accessing" may refer to any number of activities related to
generating, retrieving or processing the MRF dictionary using, for
example, MRI system 100 (shown in FIG. 1), an external network,
information repository, or combinations thereof. The MRF dictionary
includes known signal evolutions (e.g., simulated signal
evolutions). In an embodiment, the MRF dictionary may be generated
using a Bloch simulation. The MRF dictionary may be stored in
memory or data storage of, for example, an MRI system (e.g., the
MRI system 100 of FIG. 1) or other computer system.
At block 208, MRF data is acquired from the region of interest in
the subject using, for example, an MRI system (e.g., MRI system 100
shown in FIG. 1). The MRF data may be acquired using a pulse
sequence such as, for example, a fast imaging with steady-state
free precession (FISP)-MRF sequence. The MRF data acquired at block
208 is stored and compared to the MRF dictionary at block 210 to
match the acquired signal evolutions with signal evolutions stored
in the MRF dictionary. "Match" as used herein refers to the result
of comparing signals. "Match" does not refer to an exact match,
which may or may not be found. A match may be the signal evolution
that most closely resembles another signal evolution. Comparing the
MRF data to the MRF dictionary may be performed in a number of ways
such as, for example, using a pattern matching, template matching
or other matching algorithms. In one embodiment, the inner products
between the normalized measured time course of each pixel and all
entries of the normalized dictionary are calculated, and the
dictionary entry corresponding to the maximum value of the inner
product is taken to represent the closest signal evolution to the
acquired signal evolution. In another embodiment, the matching may
be performed using orthogonal matching pursuit (OMP). Quantitative
values of at least one tissue property of the MRF data are
determined based on the matching dictionary entry or entries
identified by the comparison at block 210. In an embodiment,
quantitative T1 and T2 values are determined based on the
comparison at block 210. At block 212, quantitative maps of the at
least one tissue property are generated from the quantitative
values determined at block 212. In an embodiment, quantitative T1
and T2 maps are generated from the quantitative T1 and T2 values
determined at block 212.
At block 214, at least one region of interest (ROI) is identified
on the quantitative maps generated at block 212, for example,
quantitative T1 and T2 maps. In an embodiment, regions of interest
may be drawn on the MRF T1 and T2 maps in both cancer suspicious
regions and normal tissue. Regions of interest may be drawn around,
for example, lesions in the transition zone (TZ) or peripheral zone
(PZ) as well as a healthy area of tissue for comparison. In one
example, a radiologist may draw regions of interest on the T1 and
T2 maps based on clinical MRI reads by another radiologist. The
mean ROI size may be, for example, 66.3 mm.sup.2. At block 216,
texture features are determined for each ROI of the quantitative
maps (e.g., quantitative T1 and T2 maps) using texture analysis. In
an embodiment, second order texture features may be calculated for
each ROI using Haralick texture features. In an embodiment,
twenty-six texture features were calculated. The Haralick texture
features may be calculated or derived using either Gray Level
Co-occurrence Matrices (GLCM) or Gray Level Run Length Matrices
(GLRLM).
A GLGM measures the spatial relationship between grayscale images.
The following description will refer to 4-bit grayscale images. To
create a GLGM, the original image is converted into a grayscale
image with quantized gray levels. FIG. 3 shows an example MRF-based
T2 map of with a region of interest (ROI) drawn on a transition
zone lesion and the ROI of the lesion after conversion to 4-bit
gray level image in accordance with an embodiment. In the example
of FIG. 3, a MRF-based T2 map 302 of a prostate with a ROI 304
drawn on a transition zone (TZ) prostate cancer lesion. The ROI 304
of the prostate cancer lesion is converted to a 4-bit gray level
image 306. Each pixel is measured, as is a "neighbor" pixel with a
specific spatial relationship to the first pixel. FIG. 4
illustrates an example GLGM in accordance with an embodiment. The
GLCM 402 is a two-dimensional matrix with the first dimension
representing the gray level value of the measured pixels 404, and
the second dimension representing the gray level value of the
neighbor pixel 406. The GLCM is populated with counts of
pixel-neighbor values. From the GLCM, texture features are
calculated. In an embodiment, nineteen texture features are
calculated including autocorrelation, cluster prominence, cluster
shade, contrast, correlation, difference entropy, difference
variance, dissimilarity, energy, entropy, homogeneity, information
measure of correlation 1, information measure of correlation 2,
inverse difference, maximum probability, sum average, sum entropy,
sum of squares: variance, and sum variance.
A GLRLM is also a matrix which represents spatial relationships in
a grayscale image with quantized gray levels. A GLRLM measures the
number of occurrences of consecutive pixels of the same value in a
single direction known as a "run". FIG. 5 illustrates an example of
a GLRLM in accordance with an embodiment. The GLRLM 502 is a
two-dimensional matrix, in which the first dimension represents the
possible gray level values 504. The second dimension represents the
length 506 of any given run. The GLRLM is then populated with the
counts of run numbers of a given length with a given gray level.
From the GLRLM, texture features are calculated. In an embodiment,
seven texture features are calculated including short run emphasis,
long run emphasis, gray level non-uniformity, run percentage, run
length non-uniformity, low gray level run emphasis, and high gray
level run emphasis.
Returning to FIG. 2, in an embodiment Spearman rank correlation
coefficients may be calculated and used to remove redundant texture
features from the texture features determined at block 216. In one
example described above with twenty-six calculated texture
features, Spearman correlation coefficients were used to remove
eight texture features. At block 218, the determined texture
features are used to characterize a tissue or tissues in the
region(s) of interest. In an embodiment, the characterization
identifies the presence of cancerous tissue in the region of
interest. In another embodiment, the characterization identifies
the grade of cancerous tissue (e.g. prostate cancer) in the region
of interest. Differences between texture features in prostate
cancer tissue and non-cancerous tissue (e.g., normal tissue or
prostatitis) may be used to characterize tissue as, for example,
prostate cancer, prostatitis, or normal prostate tissue (peripheral
zone or transition zone). In addition, differences between texture
features in grades of prostate cancer may be used to grade
identified cancerous tissue. In an example, the values of one or
more of the determined texture features may be used to
differentiate between prostate cancer tissue and normal or
non-cancerous prostate tissue to identify prostate tumors. The
texture features may be correlated with the gold standard
pathologic diagnosis. For example, the texture features may be
correlated with low grade cancer (e.g., GS 6), intermediate grade
cancer (e.g., GS 7), high grade cancer (e.g., GS>8) and
prostatitis, as well as normal prostate tissue. Student's t-test
may be used to differentiate between prostate cancer and normal
tissue based on the values of one or more texture features of the
region of interest. In an embodiment, Student's t-tests may be
performed on texture features between different diagnoses (e.g., GS
6-9 cancer and prostatitis) to characterize lesions. Bonferroni
correction may be applied to correct for multiple comparisons.
The example results illustrated in FIGS. 6A-6D, 7 and 8 demonstrate
that texture features of MRF derived relaxometry may be used to
quantitatively differentiate cancer and non-cancerous tissue (e.g.,
prostate cancer, prostatitis, and normal prostate tissue). The
results shown in FIGS. 6A-6D, 7 and 8 were obtained by applying the
methods described herein to an example dataset. FIGS. 6A-6D
illustrate example boxplots of texture features for comparison of
significant texture features between cancer and non-cancer TZ ROIs.
In FIG. 6A, the boxplot 602 shows example values of the texture
feature T1 Energy for all cancer 604 compared to prostatitis 606
from analysis of the example dataset. In FIG. 6B, the boxplot 608
shows example values of the texture feature T1 Entropy for all
cancer 610 compared to prostatitis 612 from analysis of the example
dataset. In FIG. 6C, boxplot 614 shows example values for the
texture feature T2 informational measure of correlation 1 (IMOC1)
for all cancer 616 compared to normal TZ tissue 618 from analysis
of the example dataset. In FIG. 6D, boxplot 620 shows example
values for the texture feature T2 Inverse difference for all cancer
622 compared to normal TZ tissue 624 from analysis of the example
dataset. In this example, after Bonferroni corrections the texture
feature T2 cluster shade was significantly different between
intermediate and high grade cancer for PZ lesions (p=0.01;
AUC=0.67). For transition zone, the texture features T.sub.1 energy
and entropy were shown to be significantly different between cancer
and prostatitis in the example analysis (p=0.011, p=0.004;
AUC=0.80, AUC=0.81 respectively) as illustrated on FIGS. 6A and 6B
and FIG. 7 (discussed below). The texture features T2 informational
measure of correlation 1 (IMOC1) and inverse difference were shown
to be significantly different between normal TZ and cancer in the
example analysis (p=0.002, p=0.016; AUC=0.86, AUC=0.74
respectively) as illustrated in FIGS. 6C, 6D and FIG. 8 (discussed
below). In addition, the texture features T.sub.1 energy and
entropy and T2 IMOC1 and inverse difference were shown to be
significantly different between TZ cancer and non-cancer tissue
(e.g., combined normal TZ tissue and prostatitis) in the example
analysis (p=0.039, p=0.020, p=0.003, p=0.048; AUC=0.72, AUC=0.71,
AUC=0.83, AUC=0.68 respectively).
At block 224, additional analyses may be performed on significant
texture features. For example, logistic regression models may be
created to calculate Receiver Operating Characteristic (ROC)
curves. In another example, Area Under the Curve's (AUC) may be
calculated. AUC values are calculated for a significant result for
significant texture features. FIG. 7 illustrates an example
Receiver Operating Characteristic (ROC) curve for two texture
features. In FIG. 7, an example ROC curve 702 for the T.sub.1
energy 704 and T1 entropy 706 texture features is shown for
comparison between all cancer and prostatitis groups for the
example dataset. In this example, AUCenergy=0.80 and
AUCentropy=0.81. FIG. 8 illustrates an example ROC curve for two
texture features in accordance with an embodiment. In FIG. 8, an
example ROC curve 802 for T2 IMOC1 804 and T2 inverse difference
806 texture features is shown for comparison between all cancer and
non-cancerous prostate groups for the example dataset. In this
example, AUCIMOC=0.86 and AUCinvdiff=0.74.
At block 220, a report may be generated indicating an output that
separates prostate cancer tissue from non-cancerous tissue in a
region of interest based on the values of the determined texture
features of the regions of interest in the quantitative T1 and T2
maps. In addition, the report may indicate an output that separates
low, intermediate and high grade cancers for identified cancer
tissue based on the values of the determined texture features in
the quantitative T1 and T2 maps. The report may include, for
example, images or maps, text or metric based reports, audio
reports and the like. For example, the report may include an image
that illustrates regions having prostate cancer tissue and regions
not having prostate cancer tissue. In another embodiment, at block
222 the quantitative ADC values (block 204) and quantitative T1 and
T2 values (block 210) may be used to generate a multi-parametric
map that may be included in the report. The multi-parametric map is
based on a combination of the quantitative ADC, T1 and T2 values.
The multi-parametric map may also be used to differentiate between
cancer and non-cancerous tissue. In another embodiment, the texture
analysis (blocks 214, 216 and 218 of FIG. 2) may be applied to the
multi-parametric map. The report may be provided to a display
(e.g., display 104, 136 or 144 shown in FIG. 1). While FIG. 2
illustrates various actions occurring in serial, it is to be
appreciated that various actions illustrates in FIG. 2 could occur
substantially in parallel.
Computer-executable instructions for characterizing a tissue in a
subject using magnetic resonance fingerprinting and texture
analysis according to the above-described methods may be stored on
a form of computer readable media. Computer readable media includes
volatile and nonvolatile, removable, and non-removable media
implemented in any method or technology for storage of information
such as computer readable instructions, data structures, program
modules or other data. Computer readable media includes, but is not
limited to, random access memory (RAM), read-only memory (ROM),
electrically erasable programmable ROM (EEPROM), flash memory or
other memory technology, compact disk ROM (CD-ROM), digital
volatile disks (DVD) or other optical storage, magnetic cassettes,
magnetic tape, magnetic disk storage or other magnetic storage
devices, or any other medium which can be used to store the desired
instructions and which may be accessed by a system (e.g., a
computer), including by internet or other computer network form of
access.
The present invention has been described in terms of one or more
preferred embodiments, and it should be appreciated that many
equivalents, alternatives, variations, and modifications, aside
from those expressly states, are possible and within the scope of
the invention. The order and sequence of any process or method
steps may be varied or re-sequenced according to alternative
embodiments.
* * * * *